Abstract
Monthly streamflow forecasting is vital for managing water resources. Recently, numerous studies have explored and evidenced the potential of artificial intelligence (AI) models in hydrological forecasting. In this study, the feasibility of the convolutional neural network (CNN), a deep learning method, is explored for monthly streamflow forecasting. CNN can automatically extract critical features from numerous inputs with its convolution–pooling mechanism, which is a distinct advantage compared with other AI models. Hydrological and large-scale atmospheric circulation variables, including rainfall, streamflow, and atmospheric circulation factors are used to establish models and forecast streamflow for Huanren Reservoir and Xiangjiaba Hydropower Station, China. The artificial neural network (ANN) and extreme learning machine (ELM) with inputs identified based on cross-correlation and mutual information analyses are established for comparative analyses. The performances of these models are assessed with several statistical metrics and graphical evaluation methods. The results show that CNN outperforms ANN and ELM in all statistical measures. Moreover, CNN shows better stability in forecasting accuracy.
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Funding
This work was supported by the National Key Research and Development Program of China [2016YFC0400906, 2017YFC0406005]; and the National Natural Science Foundation of China [91547111].
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YP designed the study. XS performed the research and wrote the initial draft of manuscript. WD analyzed the data and made revisions to the draft. ZW contributed to the revisions. JW contributed to the revisions. ML contributed to the revisions.
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Highlights
• CNN is investigated for monthly streamflow forecasting.
• The input selection process can be automatically completed by CNN.
• The performance of CNN is superior to ANN and ELM, with smaller errors and better stability.
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Shu, X., Ding, W., Peng, Y. et al. Monthly Streamflow Forecasting Using Convolutional Neural Network. Water Resour Manage 35, 5089–5104 (2021). https://doi.org/10.1007/s11269-021-02961-w
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DOI: https://doi.org/10.1007/s11269-021-02961-w